Hello, I am new to Machine Learning and Kaggle. I have participated in a few beginner competitions and got successful entries, but this is totally new.\nCan someone suggest a starting point/a beginner model for this task?\nI'm guessing transfer learning will make more sense here!
\n\nThank You in advance.
", "messages": [ { "id": 862732, "postDate": "2020-05-26T19:23:41.227Z", "content": "Did you try your hand at the TItanic competition? That's a great starting point for binary classification.
\n\nWith random datasets, try the following:\n1. Binary Classification(using Logistic Regression, Random Forest, XGBoost, CatBoost, etc.)\n2. Regression(using Linear Regression, Random Forest, XGBoost, GradientBoostingRegressor, etc.)
\n\nJust Google \"Binary classification with logistic regression python\" and you'll get a general idea.
", "rawMarkdown": "Did you try your hand at the TItanic competition? That's a great starting point for binary classification.\n\nWith random datasets, try the following:\n1. Binary Classification(using Logistic Regression, Random Forest, XGBoost, CatBoost, etc.)\n2. Regression(using Linear Regression, Random Forest, XGBoost, GradientBoostingRegressor, etc.)\n\nJust Google \"Binary classification with logistic regression python\" and you'll get a general idea.", "votes": 1 }, { "id": 862444, "postDate": "2020-05-26T15:38:01.810Z", "content": "@kingrocks95 If you are new to ML,Kaggle first start learning courses and have knowledge about models,algorithms and start creating models \nImport csv file using pandas\n2.Read csv file using pandas and display all the columns/dataset from the csv file\n[1&2 will help you understand how to read dataset which is the first step for models]\n3.Have some knowledge on Libraries like numpy,pandas,keras.etc\n4.Have some knowledge on Algorithms,mathematical logic happening in each algorithms like [Decision Tree,Random forest,linear regression]\nChoose the column from dataset for which predictions to be done and fit that into the model\n6.Split the data into test set and train set and some more steps you can learn how ML model works by learing thru below reference\nREFER = https://www.kaggle.com/learn/intro-to-machine-learning\nYou can choose Kaggle courses to get comfortable on programming ML\nREFER = https://www.kaggle.com/learn/intro-to-machine-learning\nhttps://www.kaggle.com/getting-started/150948#846595\nhttps://www.linkedin.com/posts/melvin-francis_workfromhome-staysafe-socialdistancing-activity-6661295283332362240-gfx4\nhttps://www.kaggle.com/getting-started/151001#846855\nhttps://www.kaggle.com/getting-started/151020\nhttps://www.linkedin.com/pulse/how-get-started-machine-learning-akshat-gupta/\nhttps://www.kaggle.com/getting-started/151238#848101\nYoutube Channels:
\n\nsentdex\n2.edureka\n3.deeplrn.ai\nHope this helps!
", "rawMarkdown": "@kingrocks95 If you are new to ML,Kaggle first start learning courses and have knowledge about models,algorithms and start creating models \nImport csv file using pandas\n2.Read csv file using pandas and display all the columns/dataset from the csv file\n[1&2 will help you understand how to read dataset which is the first step for models]\n3.Have some knowledge on Libraries like numpy,pandas,keras.etc\n4.Have some knowledge on Algorithms,mathematical logic happening in each algorithms like [Decision Tree,Random forest,linear regression]\nChoose the column from dataset for which predictions to be done and fit that into the model\n6.Split the data into test set and train set and some more steps you can learn how ML model works by learing thru below reference\nREFER = https://www.kaggle.com/learn/intro-to-machine-learning\nYou can choose Kaggle courses to get comfortable on programming ML\nREFER = https://www.kaggle.com/learn/intro-to-machine-learning\nhttps://www.kaggle.com/getting-started/150948#846595\nhttps://www.linkedin.com/posts/melvin-francis_workfromhome-staysafe-socialdistancing-activity-6661295283332362240-gfx4\nhttps://www.kaggle.com/getting-started/151001#846855\nhttps://www.kaggle.com/getting-started/151020\nhttps://www.linkedin.com/pulse/how-get-started-machine-learning-akshat-gupta/\nhttps://www.kaggle.com/getting-started/151238#848101\nYoutube Channels:\n\nsentdex\n2.edureka\n3.deeplrn.ai\nHope this helps!", "votes": 1 }, { "id": 862283, "postDate": "2020-05-26T14:14:06.290Z", "content": "To get started with a competition, you can always go to the notebooks section and start using some baseline models! :)
", "rawMarkdown": "To get started with a competition, you can always go to the notebooks section and start using some baseline models! :)", "votes": 1 }, { "id": 862262, "postDate": "2020-05-26T14:01:37.853Z", "content": "Hello, I am new to Machine Learning and Kaggle. I have participated in a few beginner competitions and got successful entries, but this is totally new.\nCan someone suggest a starting point/a beginner model for this task?\nI'm guessing transfer learning will make more sense here!
\n\nThank You in advance.
", "rawMarkdown": "Hello, I am new to Machine Learning and Kaggle. I have participated in a few beginner competitions and got successful entries, but this is totally new.\nCan someone suggest a starting point/a beginner model for this task?\nI'm guessing transfer learning will make more sense here!\n\nThank You in advance.", "votes": 1 }, { "id": 864306, "postDate": "2020-05-28T00:11:47.007Z", "content": "If you want to start with this particular competition, you can check my starting pack, though in these kernels I don't go into discussion of the basic concepts. If you want to clarify them, you can consider links give below and some online deep learning courses. Specifically, I quite recommend fast.ai course by Jeremy: despite I took it several years ago, it was extremely helpful.
\n\nAlso you always can go to a list of public kernels and sort them based on the submission score and the number of votes to find something that is most relevant to you (u also can do search based on keywords). Specifically, there is a number of good pure Pytorch kernels if u don't want to use fast.ai library as well as Tenserflow and Keras kernels if u prefer these frameworks.
", "rawMarkdown": "If you want to start with this particular competition, you can check my [starting pack](https://www.kaggle.com/c/prostate-cancer-grade-assessment/discussion/146855), though in these kernels I don't go into discussion of the basic concepts. If you want to clarify them, you can consider links give below and some online deep learning courses. Specifically, I quite recommend fast.ai course by Jeremy: despite I took it several years ago, it was extremely helpful. \n\nAlso you always can go to a list of public kernels and sort them based on the submission score and the number of votes to find something that is most relevant to you (u also can do search based on keywords). Specifically, there is a number of good pure Pytorch kernels if u don't want to use fast.ai library as well as Tenserflow and Keras kernels if u prefer these frameworks.", "votes": 2 } ], "comments": [ { "id": 862732, "author_name": "G_R_S", "author_url": "", "post_date": "2020-05-26T19:23:41.227000", "content": "Did you try your hand at the TItanic competition? That's a great starting point for binary classification.
\n\nWith random datasets, try the following:\n1. Binary Classification(using Logistic Regression, Random Forest, XGBoost, CatBoost, etc.)\n2. Regression(using Linear Regression, Random Forest, XGBoost, GradientBoostingRegressor, etc.)
\n\nJust Google \"Binary classification with logistic regression python\" and you'll get a general idea.
", "votes": 1, "replies": [] }, { "id": 862444, "author_name": "Pavithra T", "author_url": "", "post_date": "2020-05-26T15:38:01.810000", "content": "@kingrocks95 If you are new to ML,Kaggle first start learning courses and have knowledge about models,algorithms and start creating models \nImport csv file using pandas\n2.Read csv file using pandas and display all the columns/dataset from the csv file\n[1&2 will help you understand how to read dataset which is the first step for models]\n3.Have some knowledge on Libraries like numpy,pandas,keras.etc\n4.Have some knowledge on Algorithms,mathematical logic happening in each algorithms like [Decision Tree,Random forest,linear regression]\nChoose the column from dataset for which predictions to be done and fit that into the model\n6.Split the data into test set and train set and some more steps you can learn how ML model works by learing thru below reference\nREFER = https://www.kaggle.com/learn/intro-to-machine-learning\nYou can choose Kaggle courses to get comfortable on programming ML\nREFER = https://www.kaggle.com/learn/intro-to-machine-learning\nhttps://www.kaggle.com/getting-started/150948#846595\nhttps://www.linkedin.com/posts/melvin-francis_workfromhome-staysafe-socialdistancing-activity-6661295283332362240-gfx4\nhttps://www.kaggle.com/getting-started/151001#846855\nhttps://www.kaggle.com/getting-started/151020\nhttps://www.linkedin.com/pulse/how-get-started-machine-learning-akshat-gupta/\nhttps://www.kaggle.com/getting-started/151238#848101\nYoutube Channels:
\n\nsentdex\n2.edureka\n3.deeplrn.ai\nHope this helps!
", "votes": 1, "replies": [] }, { "id": 862283, "author_name": "Gajendra Saraswat", "author_url": "", "post_date": "2020-05-26T14:14:06.290000", "content": "To get started with a competition, you can always go to the notebooks section and start using some baseline models! :)
", "votes": 1, "replies": [] }, { "id": 864306, "author_name": "Iafoss", "author_url": "", "post_date": "2020-05-28T00:11:47.007000", "content": "If you want to start with this particular competition, you can check my starting pack, though in these kernels I don't go into discussion of the basic concepts. If you want to clarify them, you can consider links give below and some online deep learning courses. Specifically, I quite recommend fast.ai course by Jeremy: despite I took it several years ago, it was extremely helpful.
\n\nAlso you always can go to a list of public kernels and sort them based on the submission score and the number of votes to find something that is most relevant to you (u also can do search based on keywords). Specifically, there is a number of good pure Pytorch kernels if u don't want to use fast.ai library as well as Tenserflow and Keras kernels if u prefer these frameworks.
", "votes": 2, "replies": [] } ], "raw_markdown_by_id": { "862732": "Did you try your hand at the TItanic competition? That's a great starting point for binary classification.\n\nWith random datasets, try the following:\n1. Binary Classification(using Logistic Regression, Random Forest, XGBoost, CatBoost, etc.)\n2. Regression(using Linear Regression, Random Forest, XGBoost, GradientBoostingRegressor, etc.)\n\nJust Google \"Binary classification with logistic regression python\" and you'll get a general idea.", "862444": "@kingrocks95 If you are new to ML,Kaggle first start learning courses and have knowledge about models,algorithms and start creating models \nImport csv file using pandas\n2.Read csv file using pandas and display all the columns/dataset from the csv file\n[1&2 will help you understand how to read dataset which is the first step for models]\n3.Have some knowledge on Libraries like numpy,pandas,keras.etc\n4.Have some knowledge on Algorithms,mathematical logic happening in each algorithms like [Decision Tree,Random forest,linear regression]\nChoose the column from dataset for which predictions to be done and fit that into the model\n6.Split the data into test set and train set and some more steps you can learn how ML model works by learing thru below reference\nREFER = https://www.kaggle.com/learn/intro-to-machine-learning\nYou can choose Kaggle courses to get comfortable on programming ML\nREFER = https://www.kaggle.com/learn/intro-to-machine-learning\nhttps://www.kaggle.com/getting-started/150948#846595\nhttps://www.linkedin.com/posts/melvin-francis_workfromhome-staysafe-socialdistancing-activity-6661295283332362240-gfx4\nhttps://www.kaggle.com/getting-started/151001#846855\nhttps://www.kaggle.com/getting-started/151020\nhttps://www.linkedin.com/pulse/how-get-started-machine-learning-akshat-gupta/\nhttps://www.kaggle.com/getting-started/151238#848101\nYoutube Channels:\n\nsentdex\n2.edureka\n3.deeplrn.ai\nHope this helps!", "862283": "To get started with a competition, you can always go to the notebooks section and start using some baseline models! :)", "862262": "Hello, I am new to Machine Learning and Kaggle. I have participated in a few beginner competitions and got successful entries, but this is totally new.\nCan someone suggest a starting point/a beginner model for this task?\nI'm guessing transfer learning will make more sense here!\n\nThank You in advance.", "864306": "If you want to start with this particular competition, you can check my [starting pack](https://www.kaggle.com/c/prostate-cancer-grade-assessment/discussion/146855), though in these kernels I don't go into discussion of the basic concepts. If you want to clarify them, you can consider links give below and some online deep learning courses. Specifically, I quite recommend fast.ai course by Jeremy: despite I took it several years ago, it was extremely helpful. \n\nAlso you always can go to a list of public kernels and sort them based on the submission score and the number of votes to find something that is most relevant to you (u also can do search based on keywords). Specifically, there is a number of good pure Pytorch kernels if u don't want to use fast.ai library as well as Tenserflow and Keras kernels if u prefer these frameworks." } }